火曜日, 5月 21

Our Algorithms Can Predict Future Disasters — Now What?

去年の3月11日なにしてた VICTIMS MAY BE LESS

California is studded with a network of sensors that can perceive almost any motion in the ground, including the slightest perturbation of the Earth’s crust. The network began as a seismology research project, to track earthquakes in this fault-ridden part of the world. But as technologies developed, the network became more sophisticated, gathering far more data than ever before. Eventually, the science of earthquake observation reached a tipping point, and became the science of earthquake prediction.

Richard Allen, director of the UC Berkeley Seismology Laboratory, now has a prototype app on his computer called ShakeAlert that emits an annoying clanging noise up to a minute before an earthquake hits his office. Most of these quakes are so small you can barely perceive them, but ShakeAlert has successfully issued warnings in advance of all three of the quakes that hit the Bay Area in the past year. And, though a minute warning may not seem like much, it’s enough time to stop a train, pull over on the freeway, initiate shutdown at a power station, or stabilize a patient in surgery. It’s the kind of warning that could be a gamechanger for people in earthquake country.

ShakeAlert is just one of a new generation of disaster prediction technologies that are changing the odds of survival in the event of earthquakes, floods, mudslides, and even famines. Using sensor networks and algorithms that model the behavior of complex systems, we’re now able to predict the future more accurately than ever before. The question now is what we’ll do with our newfound power to look five minutes into the future. It’s something I’ve been asking scientists and engineers about over the past two years, while researching my book Scatter, Adapt and Remember: How Humans Will Survive a Mass Extinction.

Seismologist Allen has some ideas. During a recent lecture at UC Berkeley, he said there’s a 63% chance of of a major quake hitting California over the next 30 years. When it happens, ShakeAlert will predict within seconds which regions of California are about to experience strong shaking. The system is good enough that it’s already being used by BART, the Bay Area’s underground train system. When ShakeAlert issues a strong shake warning, BART trains automatically slow down to prevent derailment. As ShakeAlert improves, Allen said, it could be used to signal for automatic shutdowns at power plants and factories. It could also send warnings to California residents’ mobile devices via SMS. Similar early warning systems already do this in Japan.

But Allen admits there’s a lot more work to do. One of the big problems with quake prediction is figuring out how large an area will be affected. Even though people in some parts of Japan got early warnings about the Tōhoku quake in 2011, for example, warnings didn’t go out to enough people in Tokyo. Our current algorithms couldn’t accurately predict how many areas would be subject to shaking.

Allen said we need algorithms that can predict the spread of quakes better, perhaps by integrating data from tremors on faults. But more importantly, we need more sensors. That’s why Allen’s group is working with Deutsche Telekom to develop another app, MyShake, which will convert mobiles into quake sensors, using their built-in accelerometers and gyroscopes. “If we could harness these accelerometers then communicate that data into the cloud, we could pull out an early quake warning,” Allen said. Already, students are testing the app, and seismologists have figured out how to distinguish between actual quakes versus the motion noise created by carrying a mobile in your pocket.

Early warning systems like ShakeAlert will save lives, but it’s not exactly altruism that drives corporate investment. Companies like IBM anticipate big profits from prognostication. This is part of what drives IBM’s Smarter Cities program, a data-driven effort that IBM’s director of the Smarter Cities Growth Initiative George Thomas described as an “operating system for cities.” One of their first customers was the city of Rio de Janeiro, in Brazil, which will be playing host to the Summer Olympics in 2016. Anticipating this enormous event, as well as the 2014 World Cup, the city partnered with IBM to solve one of its worst problems: catastrophic flooding and mudslides.

A few years ago, a flash flood killed dozens of people before the city could evacuate its favelas. But now, using a sensor network that measures rainfall outside the city, Rio’s early warning system is able to predict floods up to three days in advance. This gives early responders plenty of time to marshal their resources, coordinate between emergency departments, and evacuate people safely. Thomas is hoping that other cities will buy into the Smarter Cities program, to predict and manage everything from crime and traffic to natural disasters.

There are also government efforts to implement similar warning systems. The state of Washington set up a flood and mudslide early warning system on the west side of Mt. Rainier, where flash floods have long been a problem. The system uses a seismic sensor network to detect the vibrations caused when debris begins to flow toward inhabited areas. By analyzing these vibrations in microseconds with computers, emergency responders are able to predict whether this is just a small flow or something more dangerous. People who live nearby get a roughly 30-45 minute warning via sirens before mudslides hit, said USGS hydrologist Richard Iverson. That is enough time to evacuate their homes and get to safer ground.

Some disasters come with a much longer lead time than quakes and mudslides – using satellites, geologists can predict famines years in advance. An international group of scientists called the Climate Hazards Group, based at UC Santa Barbara, uses a satellite to measure the wavelengths of light bouncing off the planet’s surface. They’re looking for one, telltale sign: how much green is covering any given stretch of landscape. The less green they see, the more likely that we’re looking at a poor harvest, and famine to follow. Using this simple warning system, the group predicted the 2011-12 famine in Somalia a year before it struck. But, as Climate Hazards Group researcher Amy McNally said, “Even with that much forewarning, response didn’t make it in time for it to not get to famine level crisis. The predictability on a year scale is there, but then you run into the political issues.”

Famine predictions highlight the trouble with knowing the future. Just because we see something coming doesn’t mean we’ll do anything about it. The problem with prediction is political will. McNally believes that the satellite warning system for famines may become good enough to predict how many famines a region will experience in a decade. But will we have international aid organizations that can soften the blow of famines we know are coming? Who will fund the preventative strategies we know are sorely needed, or create markets for systems that could?

While regions like Rio turn to corporations to help prevent disasters, others depend on government funding. UC Berkeley’s Allen is pinning his hopes on California Senate Bill 135, introduced earlier this year, which would allocate funding to build out an earthquake early warning system for California modeled on ShakeAlert. Allen estimates it will cost $23 million to build, and an additional $11 million annually to maintain it. Given that this system will save lives and infrastructure, it could ultimately save the state billions in post-quake repair bills.

Still, it remains to be seen whether politicians and citizens will act on our newfound ability to predict future disaster. It’s possible that all our predictive models make us no better than the mythological character Cassandra, gifted with an ability to see the future, and cursed to live among people who never took her predictions seriously.